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Short-term Wind Speed Prediction Method along High-speed Railway Based on SABO-LSTM
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Zhao-ji NIU1, De-cang LI1, 2, 3, *, Ru-xun XU1, 2, 3, Xiao-qiang CHEN1, 2, 3
Science Technology and Engineering | 2025, 25(9) : 3880 - 3887
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Science Technology and Engineering | 2025, 25(9): 3880-3887
Papers·Traffics and Transportations
Short-term Wind Speed Prediction Method along High-speed Railway Based on SABO-LSTM
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Zhao-ji NIU1, De-cang LI1, 2, 3, *, Ru-xun XU1, 2, 3, Xiao-qiang CHEN1, 2, 3
Affiliations
  • 1 Mechatronics T & R Institute, Lanzhou Jiaotong University, Lanzhou 730070, China
  • 2 Gansu Provincial Engineering Technology Center for Information of Logistics & Transport Equipment, Lanzhou 730070, China
  • 3 Gansu Provincial Industry Technology Center of Logistics & Transport Equipment, Lanzhou 730070, China
Published: 2025-03-28 doi: 10.12404/j.issn.1671-1815.2403792
Outline
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Accurate prediction of wind speed along high-speed rail lines is a fundamental requirement for railway disaster warning systems. To enhance the capability to respond to and handle sudden events caused by strong winds, a short-term wind speed prediction method based on the subtraction average based optimizer (SABO) algorithm optimized long short-term memory (LSTM) neural network was proposed. Firstly, considering the nonlinearity and non-stationarity of wind speed, the min-max (MM) method was used to normalize the wind speed data. Secondly, the “-v” method in the SABO algorithm was employed to search and optimize the key parameters of the LSTM model, constructing a wind speed prediction model. Finally, the effectiveness of the model was tested using measured wind speed data collected from wind speed collection points along the Baoji-Lanzhou high-speed railway in China. Experimental results show that the SABO algorithm’s optimization effect is better, and the prediction accuracy is higher. The average absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of the constructed model are 11.96%, 1.23%, and 16.47%, respectively, with a coefficient of determination (R2) of 0.995. Compared to other models, the LSTM neural network optimized by the SABO algorithm exhibits better fitting performance and higher prediction accuracy in short-term wind speed prediction, providing a new method and approach for wind prediction and warning along high-speed railway.

high-speed railway  /  wind speed prediction  /  subtraction average based optimizer (SABO)  /  long short-term memory (LSTM) neural networks
Zhao-ji NIU, De-cang LI, Ru-xun XU, Xiao-qiang CHEN. Short-term Wind Speed Prediction Method along High-speed Railway Based on SABO-LSTM[J]. Science Technology and Engineering, 2025 , 25 (9) : 3880 -3887 . DOI: 10.12404/j.issn.1671-1815.2403792
Year 2025 volume 25 Issue 9
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Article Info
doi: 10.12404/j.issn.1671-1815.2403792
  • Receive Date:2024-05-22
  • Online Date:2025-07-09
  • Published:2025-03-28
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  • Received:2024-05-22
  • Revised:2024-12-29
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Affiliations
    1 Mechatronics T & R Institute, Lanzhou Jiaotong University, Lanzhou 730070, China
    2 Gansu Provincial Engineering Technology Center for Information of Logistics & Transport Equipment, Lanzhou 730070, China
    3 Gansu Provincial Industry Technology Center of Logistics & Transport Equipment, Lanzhou 730070, China
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表12种不同金属材料的力学参数

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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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